The Oldest Pepper Tree in California
At one point in time, search engines such as Google learned about topics on the Web from sources such as Yahoo! and the Open Directory Project, which provided categories of sites, within directories that people could skim through to find something that they might be interested in.
Those listings of categories included hierarchical topics and subtopics; but they were managed by human beings and both directories have closed down.
In addition to learning about categories and topics from such places, search engines used to use such sources to do focused crawls of the web, to make sure that they were indexing as wide a range of topics as they could.
It’s possible that we are seeing those sites replaced by sources such as Wikipedia and Wikidata and Google’s Knowledge Graph and the Microsoft Concept Graph.
Last year, I wrote a post called, Google Patents Context Vectors to Improve Search. It focused upon a Google patent titled User-context-based search engine.
In that patent we learned that Google was using information from knowledge bases (sources such as Yahoo Finance, IMDB, Wikipedia, and other data-rich and well organized places) to learn about words that may have more than one meaning.
An example from that patent was that the word “horse” has different meanings in different contexts.
To an equestrian, a horse is an animal. To a carpenter, a horse is a work tool when they do carpentry. To a gymnast, a horse is a piece of equipment that they perform manuevers upon during competitions with other gymnasts.
A context vector takes these different meanings from knowledge bases, and the number of times they are mentioned in those places to catalogue how often they are used in which context.
I thought knowing about context vectors was useful for doing keyword research, but I was excited to see another patent from Google appear where the word “context” played a featured role in the patent. When you search for something such as a “horse”, the search results you recieve are going to be mixed with horses of different types, depending upon the meaning. As this new patent tells us about such search results:
The ranked list of search results may include search results associated with a topic that the user does not find useful and/or did not intend to be included within the ranked list of search results.
If I was searching for a horse of the animal type, I might include another word in my query that identified the context of my search better. The inventors of this new patent seem to have a similar idea. The patent mentions
In yet another possible implementation, a system may include one or more server devices to receive a search query and context information associated with a document identified by the client; obtain search results based on the search query, the search results identifying documents relevant to the search query; analyze the context information to identify content; and generate a group of first scores for a hierarchy of topics, each first score, of the group of first scores, corresponding to a respective measure of relevance of each topic, of the hierarchy of topics, to the content.
From the pictures that accompany the patent it looks like this context information is in the form of Headings that appear above each search result that identify Context information that those results fit within. Here’s a drawing from the patent showing off topical search results (showing rock/music and geology/rocks):
Different types of ‘rock’ on a search for ‘rock’ at Google
This patent does remind me of the context vector patent, and the two processes in these two patents look like they could work together. This patent is:
Context-based filtering of search results
Inventors: Sarveshwar Duddu, Kuntal Loya, Minh Tue Vo Thanh and Thorsten Brants
Assignee: Google Inc.
US Patent: 9,779,139
Granted: October 3, 2017
Filed: March 15, 2016
Abstract
A server is configured to receive, from a client, a query and context information associated with a document; obtain search results, based on the query, that identify documents relevant to the query; analyze the context information to identify content; generate first scores for a hierarchy of topics, that correspond to measures of relevance of the topics to the content; select a topic that is most relevant to the context information when the topic is associated with a greatest first score; generate second scores for the search results that correspond to measures of relevance, of the search results, to the topic; select one or more of the search results as being most relevant to the topic when the search results are associated with one or more greatest second scores; generate a search result document that includes the selected search results; and send, to a client, the search result document.
It will be exciting to see topical search results start appearing at Google.
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Source: SEO by the Sea